The BCI competition III: validating alternative approaches to actual BCI problems
Open accessAbstract
A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.
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- Authors
- Blankertz, B., Muller, K.-R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millan, J. R., Schroder, M., & Birbaumer, N.
- Affiliations
- Fraunhofer-Gesellschaft (DE)
- New York State Department of Health (US)
- Graz University of Technology (AT)
- University of Tübingen (DE)
- University of Trento (IT)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2006-06
- Volume
- 14
- Issue
- 2
- Pages
- 153-159
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2101629643
- Cited by
- 927
- References
- 24
- Citations by year
- 2026: 36 · 2025: 41 · 2024: 58 · 2023: 71 · 2022: 59 · 2021: 67 · 2020: 56 · 2019: 60 · 2018: 48 · 2017: 48 · 2016: 57 · 2015: 36 · 2014: 47 · 2013: 49 · 2012: 36
- FWCI
- 12.94
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- publish.UP (University of Potsdam) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- National University of Defense Technology
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, R01EB000856)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- brain-computer interface, BCI Competition III, pattern classification, signal analysis
- MeSH terms
- Algorithms, Brain, Communication Devices for People with Disabilities, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Software Validation, Technology Assessment, Biomedical, User-Computer Interface, Databases, Factual
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